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Department
Lecturer
Course
Course No.
COLLEGE OF ENGINEERING & TECHNOLOGY
: .. Department of Computer Science ..……..........................………............
: .. Dr. Ibrahim Imam ....................................………............
: .. Introduction to Artificial Intelligence ..........……............
: .. CS 366 ……….. Sheet : ..……4..........
Q1. Suppose you have the following neural network, the weight between node xi and
node xj is given by:
(xi - xj) /(xi + xj)
What decision shall be given to each of the cases in the following table? Can you
drive a logical expression equivalent to this neural network? Explain.
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Q2. Give one difference between:
• Supervised learning algorithm and supervised neural network algorithm
• Expert system and expert system shell
• Search and search space
• Frames and decision rules
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Q3. Consider the following trained neural network. Use the sigmoid function to obtain
the input and output value to each node (as I shown you in the lecture).
• Use the test data in the given table to test the neural network. Calculate the
decision provided by this neural network for each record/example.
• Can you represent the decision column as a logical relationship using the three
attributes?
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Q4. Suppose you are given the following trained neural network for the data to the
right.
Can you calculate the missing weight? If yes, show how.
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Q5. Given a database that contains four (4) attributes, W, X, Y, Z, and a decision
attribute D. Design the input and output layers for a neural network in two (2)
different ways given that the attribute domains are: W = {0, 2, 4}; X = {k, n}; Y =
{10, 12, 14, 16, 18}; Z = {True, False} and the decisions are D = {a, b, c}. What is
the input vector to the neural network for the example:
(W=2)(X=n)(Y=10)(Z=True)(D=c)
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Q6. Given a database that contains four (4) attributes, W, X, Y, Z, and a decision
attribute D. Design the input and output layers for a neural network in two (2)
different ways given that the attribute domains are: W = {0, 2, 4}; X = {k, n}; Y =
{10, 12, 14, 16, 18}; Z = {True, False} and the decisions are D = {a, b, c}.